Carbon Central reports that NSW Land and Environment Court has thrown out most of Australia's first climate change case, agreeing with Macquarie Generation its licence allows it to emit CO2.
NSW Environment Defender’s Office (EDO) argued that the Bayswater coal-fired power station breached its operating licence under the NSW Protection of the Environment Operations Act (POEO) by negligently emitting CO2.
Justice Pain ruled the POEO Act gave it “implied authority†to emit CO2.
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Problem 2. Cointegration was developed in economics to deal with a problem of spurious correlation between series with stochastic trends. Why should spurious correlation be a concern if the trends in temperature and GHGs are deterministic?
Sometimes I've been accused of over-simplifying, but I do try to make models as simple as possible, because it avoids a lot of speculation. With that view, this simple model represents paradoxical features of unit roots. Even if there was a deterministic relation
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The acronym ARIMA stands for "Auto-Regressive Integrated Moving Average." Random-walk and random-trend models, autoregressive models, and exponential smoothing models (i.e., exponential weighted moving averages) are all special cases of ARIMA models. An ARIMA model is classified as an "ARIMA(p,d,q)" model, where the current value y is determined by:
* p -- the number of lagged terms (AR),
* d -- the number of integrations, and
* q -- the number of moving average terms (MA).
Here is
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Problem 5. Why do most of the forecasts of climate science fail?
If climate science had a history of accurate forecasts, it would have a foundation for greater credibility. That is what is expected in other fields. Instead, it is "denialist" to say that climate science has a lousy record of predictions.
When I started analysing ecological models in my doctoral studies, it wasn't ideologically unsound to say that the models did a lousy job, and I spent 3 years trying to work out why. Wouldn't
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Problem 3. Why is the concept of ‘climate’ distinguished from the concept of ‘weather’ by an arbitrary free parameter, usually involved in averaging or smoothing or ’scale’ transformations of 10 to 30 years?
The recent article on Question #9 by Meiers and response by Stephen Goddard used a coin toss analogy to answer this question. Meiers states that while the uncertainty of the probability of heads in the short term is high, over the long term we expect
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Problem 1. If temperature is adequately represented by a deterministic trend due to increasing GHGs, why be concerned with the presence of a unit root?
Rather than bloviate over the implications of a unit root (integrative behavior) in the global temperature series, a more productive approach is to formulate an hypothesis, and test it.
A deterministic model of global temperature (y) and anthropogenic forcing (g) with random errors e is:
yt=a+b.gt+ε
An autoregressive model of changes in
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Prompted by the interest VS has rekindled in fundamental analysis of the temperature series at Bart's and Lucia's blogs, below are a small set of core 'problems' facing statistical climate science (CS) -- kind of a challenge.
Remember a deterministic trend is one brought about by a changing value of the mean, due to a change in an equilibrium value for example (ie non-stationary). A stochastic trend is due the accumulation of random variations; all parameters are stationary.
Problem 1. If temperature
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